Hadoop自定义类型处理手机上网日志

job提交源码分析

在eclipse中的写的代码如何提交作业到JobTracker中的哪？
(1)在eclipse中调用的job.waitForCompletion(true)实际上执行如下方法
　　connect();
　　info = jobClient.submitJobInternal(conf);
(2)在connect()方法中，实际上创建了一个JobClient对象。
　　在调用该对象的构造方法时，获得了JobTracker的客户端代理对象JobSubmissionProtocol。
　　JobSubmissionProtocol的实现类是JobTracker。
(3)在jobClient.submitJobInternal(conf)方法中，调用了
　　JobSubmissionProtocol.submitJob(...),
　　即执行的是JobTracker.submitJob(...)。

Hadoop数据类型

1.Hadoop的数据类型要求必须实现Writable接口。
2.java基本类型与Hadoop常见基本类型的对照
　　　　Long 　　　　LongWritable
　　　　Integer 　　 IntWritable
　　　　Boolean 　　 BooleanWritable
　　　　String 　　　 Text

java类型如何转化为hadoop基本类型？
　　　　调用hadoop类型的构造方法，或者调用set()方法。
　　　　　　new LongWritable(123L);

hadoop基本类型如何转化为java类型？
　　　　对于Text，需要调用toString()方法，其他类型调用get()方法。

使用Hadoop自定义类型处理手机上网日志

1、首先，将手机上网日志文件HTTP_20130313143750.dat通过WinSCP工具复制到/usr/local目录下

2、将日志文件上传到hdfs://chaoren:9000/wlan文件夹下



日志文件：



日志文件中各字段含义：



3、编写Java代码将日志文件中想要的数据统计出来。

1 package mapreduce;
2
3 import java.io.DataInput;
4 import java.io.DataOutput;
5 import java.io.IOException;
6
7 import org.apache.hadoop.conf.Configuration;
8 import org.apache.hadoop.fs.Path;
9 import org.apache.hadoop.io.LongWritable;
10 import org.apache.hadoop.io.Text;
11 import org.apache.hadoop.io.Writable;
12 import org.apache.hadoop.mapreduce.Job;
13 import org.apache.hadoop.mapreduce.Mapper;
14 import org.apache.hadoop.mapreduce.Reducer;
15 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
16 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
17 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
18 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
19 import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
20
21 public class KpiApp {
22     static final String INPUT_PATH = "hdfs://chaoren:9000/wlan";//wlan是个文件夹，日志文件放在/wlan目录下
23     static final String OUT_PATH = "hdfs://chaoren:9000/out";
24
25     public static void main(String[] args) throws Exception {
26         final Job job = new Job(new Configuration(),
27                 KpiApp.class.getSimpleName());
28         // 1.1 指定输入文件路径
29         FileInputFormat.setInputPaths(job, INPUT_PATH);
30         // 指定哪个类用来格式化输入文件
31         job.setInputFormatClass(TextInputFormat.class);
32
33         // 1.2指定自定义的Mapper类
34         job.setMapperClass(MyMapper.class);
35         // 指定输出<k2,v2>的类型
36         job.setMapOutputKeyClass(Text.class);
37         job.setMapOutputValueClass(KpiWritable.class);
38
39         // 1.3 指定分区类
40         job.setPartitionerClass(HashPartitioner.class);
41         job.setNumReduceTasks(1);
42
43         // 1.4 TODO 排序、分区
44
45         // 1.5 TODO （可选）归约
46
47         // 2.2 指定自定义的reduce类
48         job.setReducerClass(MyReducer.class);
49         // 指定输出<k3,v3>的类型
50         job.setOutputKeyClass(Text.class);
51         job.setOutputValueClass(KpiWritable.class);
52
53         // 2.3 指定输出到哪里
54         FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
55         // 设定输出文件的格式化类
56         job.setOutputFormatClass(TextOutputFormat.class);
57
58         // 把代码提交给JobTracker执行
59         job.waitForCompletion(true);
60     }
61
62     static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable> {
63         protected void map(
64                 LongWritable key,
65                 Text value,
66                 org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, KpiWritable>.Context context)
67                 throws IOException, InterruptedException {
68             final String[] splited = value.toString().split("\t");
69             final String msisdn = splited[1];
70             final Text k2 = new Text(msisdn);
71             final KpiWritable v2 = new KpiWritable(splited[6], splited[7],
72                     splited[8], splited[9]);
73             context.write(k2, v2);
74         };
75     }
76
77     static class MyReducer extends
78             Reducer<Text, KpiWritable, Text, KpiWritable> {
79         /**
80          * @param k2
81          *            表示整个文件中不同的手机号码
82          * @param v2s
83          *            表示该手机号在不同时段的流量的集合
84          */
85         protected void reduce(
86                 Text k2,
87                 java.lang.Iterable<KpiWritable> v2s,
88                 org.apache.hadoop.mapreduce.Reducer<Text, KpiWritable, Text, KpiWritable>.Context context)
89                 throws IOException, InterruptedException {
90             long upPackNum = 0L;
91             long downPackNum = 0L;
92             long upPayLoad = 0L;
93             long downPayLoad = 0L;
94
95             for (KpiWritable kpiWritable : v2s) {
96                 upPackNum += kpiWritable.upPackNum;
97                 downPackNum += kpiWritable.downPackNum;
98                 upPayLoad += kpiWritable.upPayLoad;
99                 downPayLoad += kpiWritable.downPayLoad;
100             }
101
102             final KpiWritable v3 = new KpiWritable(upPackNum + "", downPackNum
103                     + "", upPayLoad + "", downPayLoad + "");
104             context.write(k2, v3);
105         };
106     }
107 }
108
109 class KpiWritable implements Writable {
110     long upPackNum;
111     long downPackNum;
112     long upPayLoad;
113     long downPayLoad;
114
115     public KpiWritable() {
116     }
117
118     public KpiWritable(String upPackNum, String downPackNum, String upPayLoad,
119             String downPayLoad) {
120         this.upPackNum = Long.parseLong(upPackNum);
121         this.downPackNum = Long.parseLong(downPackNum);
122         this.upPayLoad = Long.parseLong(upPayLoad);
123         this.downPayLoad = Long.parseLong(downPayLoad);
124     }
125
126     public void readFields(DataInput in) throws IOException {
127         this.upPackNum = in.readLong();
128         this.downPackNum = in.readLong();
129         this.upPayLoad = in.readLong();
130         this.downPayLoad = in.readLong();
131     }
132
133     public void write(DataOutput out) throws IOException {
134         out.writeLong(upPackNum);
135         out.writeLong(downPackNum);
136         out.writeLong(upPayLoad);
137         out.writeLong(downPayLoad);
138     }
139
140     @Override
141     public String toString() {
142         return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t"
143                 + downPayLoad;
144     }
145 }

4、运行结果